Non-invasive and non-contact measurement in early therapeutic intervention

ABSTRACT

Systems and methods for non-invasive and/or non-contact measurement of a subject&#39;s tremor in the context of therapeutic intervention is presented. The system includes a memory, a communications interface, a sensor to measure a signal associated with position or motion of an extremity of the subject, and a processor. The processor is configured to receive the signal from the sensor. The processor is further configured to communicate, via the communications interface, the signal, login credentials, and position or motion data to a remote server coupled to a remote electronic health record database. The remote server is configured to receive the signal from the sensor. The processor is configured to receive an analysis that quantified the severity of tremor.

RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.15/766,805, filed on Apr. 6, 2018, which is a 371 National Stage Entryof PCT/US2016/056476, filed on Oct. 11, 2016, which claims the benefitunder 35 U.S.C. § 119(e) of U.S. Provisional Application No. 62/239,604,filed Oct. 9, 2015.

TECHNICAL FIELD

The present disclosure relates to non-invasive and/or non-contactmeasurement of a subject's tremor in the context of therapeuticintervention. The present disclosure more particularly relates to a)identifying promising clinical drug candidates in pharmaceuticaldevelopment by confirming therapeutic target engagement in-vivo, b)screening for and diagnosis of early disease such as prodromalParkinson's Disease (PD), and c) in-office as well as home and portablemonitoring of therapeutic interventions.

BACKGROUND

Tremors are typically defined as abnormal and oscillatory movements ofthe body and are one of the first and most recognized physical symptomsof Parkinson's disease (PD). As with all progressive diseases, thesymptoms of Parkinson's disease typically worsen over time and tremor isno exception.

In a recently published paper in Lancet Neurology (Schrag et al, 2014),the most common prediagnostic symptom of Parkinson's disease within 2years before diagnosis (n=7232) was tremor, with 41% of individualsreporting symptoms to their General Practitioner (GP) compared with lessthan 1% of controls (n=40541). Further, Postuma et al (2015) reportedtremor as a prodromal symptom with a 2.3× odds ratio for conversion inRBD subjects vs RBD non-converters.

The current clinical standard for measuring tremor in Parkinson'sdisease is the Unified Parkinson Disease Rating Scale (UPDRS) scoring,where a clinician performs a qualitative assessment resulting in ascore.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates a block diagram of an example operating environmentof a diagnostic and/or therapeutic intervention system that measures andanalyzes the severity of limb tremor;

FIG. 2 illustrates a sequence diagram of an example method to measureand analyze tremor in a system that includes a peripheral measurementdevice connected to a client device;

FIG. 3 illustrates a sequence diagram of an example method to measureand analyze tremor in a system that includes a client device with anintegrated peripheral measurement device;

FIG. 4 illustrates a simulator system;

FIG. 5 illustrates an isometric view of one embodiment of a simulator;

FIG. 6 illustrates sample data obtained using a simulator;

FIG. 7 illustrates calculated statistics of sample data for thecalculated displacement of the magnitude of the X, Y, and Z axes;

FIG. 8 illustrates sample results of calculated frequencies from 20trials (tests) of 20 second recordings, with known frequencies using asimulator;

FIG. 9 illustrates sample results of calculated amplitudes of movementfrom 20 trials of 20 second recordings, with consistent amplitudes usinga simulator;

FIG. 10 illustrates sample results of calculated frequencies from 10trials of 10 second recordings, with known frequencies using asimulator;

FIG. 11 illustrates sample results of calculated amplitudes from 10trials of 10 second recordings, with consistent amplitudes using asimulator;

FIG. 12 illustrates a plot of sample data of actual frequency versuscalculated frequency, for various different known frequencies along witha line of best fit and relevant statistics;

FIG. 13 illustrates an embodiment of the pseudocode pipeline;

FIG. 14 illustrates an embodiment of the pseudocode pipeline for thedata analysis, performed either in the client software or data analyticsserver; and

FIG. 15 illustrates a diagrammatic representation of a machine in theexample form of a computing device within which a set of instructions,for causing the machine to perform any one or more of the methodologiesdiscussed herein, may be executed,

all arranged in accordance with at least one embodiment describedherein.

DETAILED DESCRIPTION

Although some quantitative methods for measuring and analyzing tremorcurrently exist, they are limited. Many of these methods require theexact placement of sensors on the human body, which may result in humanerror and may distort results. Laser displacement and velocity systemsare sometimes accurate, but may be too delicate and may requireextensive setup for practical clinical or research use. Surfaceelectromyography (EMG) is not sensitive enough to provide usefulmeasurements of low-amplitude tremor, and the additional weight ofaccelerometers attached to the body can produce distorted results.

Conventional commercially available systems may not allow noninvasiveand/or non-contact tremor measurement, and suitable for use in thecontext of early disease therapeutic intervention such as during drugdevelopment, and beyond in clinical application thereafter. Currently,many of the commercially available systems may require some type ofphysical contact to be made with a sensor, such as being affixed to thebody part to be measured. Most direct approaches of assessing drugtarget engagement in humans during drug development, such as molecularassays or molecular imaging probes, may either require an invasive stepsuch as blood sampling, or use of radiation and/or imaging agents.

Aspects of the present disclosure may relate to accurately andobjectively quantify the severity of tremor using a non-contactmeasurement for use in the context of early therapeutic intervention,particularly with disease-modifying drug therapy in PD. Non-drug earlyintervention approaches such as brain stimulation (with DBS, TMS, tDCS,TENS, etc.), focused ultrasound, and the like could also be used. Thepresent disclosure further allows for the analysis and storage of theseverity of tremor at a separate remote location such as from a clientcomputer with low processing power and in a clinical drug trial or fortelemedicine use case. The disclosure further provides in-office as wellas home and mobile monitoring of the results of the analysis. Inaddition, the disclosure allows physicians or clinicians remote accessto the results of the analysis.

In one embodiment, the present disclosure includes a limb trackingsystem that includes a sensor that is capable of measuring signalscorresponding with a subject's limb positions over time withoutrequiring the subject to wear or attach any external sensors to thebody. The signal is then transmitted either via wired or wirelessconnection to the client software, located on a personal computer. Theclient software then calculates the severity of the subject's handtremor based on the three-dimensional position or motion signals fromthe sensor. The calculated severity is then compared to at least onereference value, coding the severity of tremor, such as with a givencolor.

In another embodiment, the present disclosure includes a hand trackingsystem that measures and transmits the position signals to a computer.The signals are electronically transferred to a data analytics unit,where the severity of the subject's hand tremor and tremorcharacteristics are calculated based on the three-dimensional positionsignals. The calculated severity is then compared to at least onereference value, coding the severity of tremor with a given color. Theresults of the tremor severity calculated in the data analytics unit arethen electronically transmitted to an Electronic Health Record (EHR)Database for storage and future retrieval. Upon storage, the clientsoftware then receives the results to display to the subject on theircomputer.

In yet another embodiment, the present disclosure includes a handtracking system that measures and transmits position signals to acomputer where it is electronically transferred to a data analytics unitto calculate the tremor severity, which then transmits the results to anElectronic Health Record (EHR) Database for storage. Upon storage, themobile application receives and displays the results to the subject on asmart phone or smart watch.

Another embodiment of the present disclosure includes a smartwatch/smart phone system, which includes at least one sensor that iscapable of measuring signals corresponding with the movement of thedevice, such as acceleration or gyroscopic data. This signal is thenreceived by a mobile application on the subject's smart phone andtransmitted to a data analytics unit, where the severity of thesubject's hand tremor as well as tremor characteristics are calculatedbased on the acceleration and gyroscope signals. The results of thetremor severity calculated in the data analytics unit are thenelectronically transmitted to an Electronic Health Record (EHR) Databasefor storage, based on login credentials. Upon storage, the mobileapplication queries for and displays the results to the subject on thesmart phone/smart watch system.

A system to analyze a subject's tremor includes a memory, acommunications interface, a sensor to measure a signal associated withposition or motion of an extremity of the subject, and a processoroperatively coupled to the memory, the communications interface, and thesensor. The processor is configured to receive the signal from thesensor. The processor is further configured to communicate, via thecommunications interface, the signal, login credentials and position ormotion data to a remote server coupled to a remote electronic healthrecord database. The remote server is configured to receive the signaland perform an analysis that quantifies a severity of tremor based onthe position or motion signals of the subject. The processor isconfigured to receive the analysis that quantified the severity oftremor and

-   -   cause the quantified severity of tremor to be displayed via a        display device.

The previous and following depictions are examples of the presentdisclosure and are intended to provide a framework to understand thecomponents, methods, and characteristics of the embodiments disclosedherein. The included drawings are provided to allow for a more detailedunderstanding of the present disclosure as well as to illustrate variousembodiments. The written descriptions together with the drawings andillustrations serve to further expand on the details of the disclosure.

The following detailed description references the accompanying drawings.The same reference numbers identify the same elements and componentsthroughout the different drawings. The following description does notencompass the entire framework and scope of the present disclosure andis therefore used to serve as a mere embodiment of the disclosure anddoes not limit the scope of the appended claims.

The present disclosure may include non-invasive and non-contactmeasurement of a subject's tremor in the context of early therapeuticintervention. The devices, systems, and methods involved in the variousembodiments described are used to measure, analyze, quantify, score,display and monitor human tremors. These tremors include, but are notlimited to, those tremors experienced in various movement disorders suchas Parkinson's disease (PD) that may occur at rest, posture, or duringan action.

FIG. 1 illustrates a block diagram of an example operating environment100 of a therapeutic intervention system, arranged in accordance with atleast one embodiment described herein. The operating environment 100includes at least one client device 101, 201, 301, a server 401, atleast one database 501, and a network 550. The client devices 101, 201,301, the server 401, the database 501, (collectively, “environmentcomponents”) may be communicatively coupled via the network 550. Theenvironment components may communicate data and information, such asmessages pertaining to questions, solutions and/or services communicatedfrom the server 401 to the client devices 101, 201, 301 via the network550. Each of the environment components is briefly described in thefollowing paragraphs.

The client devices 101, 201, 301 may include a processor-based computingsystem. The client devices 101, 201, 301 each may include memory, aprocessor, and network communication capabilities. In the operatingenvironment 100, the client devices 101, 201, 301 may be capable ofcommunicating and receiving data and information to and from the server401 via the network 550. Some examples of the client devices 101, 201,301 may include a mobile phone, a smartphone, a tablet computer, alaptop computer, a desktop computer, a set-top box, a wearable device, awatch, a smart watch, a virtual-reality device, or a connected device,etc. The client devices 101, 201, 301 may include one or more sensors todetect information pertaining to a user of the client devices 101, 201,301, an environment in which the client devices 101, 201, 301 issituated, etc. The one or more sensors may include at least one of aclock, camera, microphone, gyroscope, accelerometer, infrared sensor,global positioning system (GPS), near-field communication (NFC) sensor,brightness sensor, proximity sensor, compass, thermometer, step counter,or fingerprint sensor, etc.

The server 401 may include a processor-based computing device. Forexample, the server 401 may include a hardware server or anotherprocessor-based computing device configured to function as a server. Theserver 401 may include memory and network communication capabilities. Inthe operating environment 100, the server 401 may be configured tocommunicate with the client device 301 and the database 501 via thenetwork 550.

The database 501 may include any memory or data storage. The database501 may include network communication capabilities such that theenvironment components may communicate with the database 501. In someembodiments, the database 501 may include computer-readable storagemedia for carrying or having computer-executable instructions or datastructures stored thereon. The computer-readable storage media mayinclude any available media that may be accessed by a general-purpose orspecial-purpose computer, such as a processor. For example, the database501 may include computer-readable storage media that may be tangible ornon-transitory computer-readable storage media including Random AccessMemory (RAM), Read-Only Memory (ROM), Electrically Erasable ProgrammableRead-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM) orother optical disk storage, magnetic disk storage or other magneticstorage devices, flash memory devices (e.g., solid state memorydevices), or any other storage medium which may be used to carry orstore desired program code in the form of computer-executableinstructions or data structures and that may be accessed by ageneral-purpose or special-purpose computer. Combinations of the abovemay be included in the database 501. The database 501 may include anElectronic Health Record (EHR) database 501.

The database 501 may store various data. The data may be stored in anydata structure, such as a relational database structure.

The network 550 may include a wired network, a wireless network, or anycombination thereof. The network 550 may include any suitable topology,configuration or configurations including a star configuration, tokenring configuration, or other configurations. The network 550 may includea local area network (LAN), a wide area network (WAN) (e.g., theInternet), and/or other interconnected data paths across which multipledevices may communicate. In some embodiments, the network 550 mayinclude a peer-to-peer network. The network 550 may also be coupled toor include portions of a telecommunications network that may enablecommunication of data in a variety of different communication protocols.In some embodiments, the network 550 includes BLUETOOTH® communicationnetworks and/or cellular communication networks for sending andreceiving data including via short messaging service (SMS), multimediamessaging service (MMS), hypertext transfer protocol (HTTP), direct dataconnection, wireless application protocol (WAP), e-mail, or the like.

As illustrated in FIG. 1, a client device (e.g., client device 101) mayinclude one or more sensors 110 that are capable of measuring asubject's three-dimensional position or motion of one or moreextremities of a subject without physical contact. These sensors 110 maybe a part of, but are not limited to, a hand tracking system (e.g.,client device 101). In accordance with at least one embodiment, the handtracking system provides non-contact motion detection of a user'sextremities for purposes of navigating interfaces by hand movements,instead of by a computer mouse and the like. This system includes amethod for detecting, measuring, and relaying three-dimensional positiondata 111 of different locations on the extremities of a user. Oneexample of such a device is marketed as “Leap Motion Controller” (LeapMotion, Inc., San Francisco, USA). Other hand tracking and motiondetecting systems can also be used within the framework. For example,various depth or 3D camera and related toolkits such as Intel RealSense,Nimble Sense, Reactiv Touch+ can be utilized.

In an embodiment, the hand tracking system (e.g., client device 101)acts as a peripheral and interfaces with a personal computer (e.g.,client device 301) through a wired (e.g., Universal Serial Bus (USB))connection. In an alternate embodiment, the hand tracking system mayinterface with the personal computer 301 via wireless methods such asBluetooth, Bluetooth Low Energy (BLE), or through a Wireless-Fidelity(Wi-Fi) service method. The personal computer 301 may include a laptop,tablet, or desktop computer. In yet another embodiment, the handtracking system 101 is integrated in a client computer 301 such as apersonal computer, or integrated in/attached to mobile phone, or anotherportable or wearable computer such as a head mounted computer, gogglesor glasses. In an embodiment, said computer such as personal computer301 runs client software 311 that may serve as the user interface toinstruct or guide the user to operate the hand tracking system 101 orrelay information such as raw data from the hand tracking system 101 oranalysis of the data to the user. Further, the client software 311 maycommunicate with the hand tracking system 101 to collect and aggregatethree-dimensional position data 111, and may control the hand trackingsystem 101.

Further, in an embodiment, the client software 311 may also performreal-time or post-processing of the three-dimensional position data 111to determine whether the data being recorded is optimal, whether theuser is performing the instructed tasks correctly, in order to minimizethe amount of error and improve the accuracy of the measurements such asthe pseudocode shown in FIG. 13. In an embodiment, once a test orrecording has started the position of the hand or fingers are constantlychecked for their three-dimensional position to ensure that the hand isin the correct position for recording or in the most optimal volumespace in relation the hand tracking system 101. Within a specifiedamount of time at the beginning of the recording, the hand or fingersare checked to ensure that they are not purposefully moving bycalculating and comparing the instantaneous velocity of the hand, whichmay include measuring the palm, against a predetermined velocitythreshold. The purpose of this procedure is to not only ensure that theuser is not moving their hand around, which could introduce error intothe tremor analysis, but also to confirm that the positions reported bythe hand tracking system 101 are accurate and not unreasonable. Thelength of time of the recording is also continually checked to ensurethat it does not exceed the pre-set time of the recording. If the lengthof time of the data recorded has not exceeded the pre-set timethreshold, a metric either provided by the hand tracking system 101 orcalculated by the data provided by the hand tracking system 101 is usedto determine the confidence level of the accuracy of the hand trackingsystem 101. If this confidence level is below a pre-determinedthreshold, the recorded data is deleted and the test as well asrecording is restarted. If the confidence level is above thepre-determined threshold, the recording continues. The hand position isalso continually checked during the recording to ensure that the hand isstill within a predetermined volume, if the hand moves out of thispredetermined volume then the recording is deleted and restarted.Similarly, the velocity of the hand is constantly checked to make surethat it doesn't pass a predetermined velocity threshold to make sure thehand is held as steady as possible and to confirm that the data from thehand tracking system 101 is reasonable and not outputting unreasonablevalues throughout the recording. If the velocity of the hand passes thepre-set velocity threshold, the recorded data is deleted and therecording is restarted. If all the conditions for ensuring correct datafrom the hand tracking system 101 and for ensuring that the user isperforming the test correctly are met, then the data is recorded and isappended to currently existing recording. Once the test duration reachesthe predetermined test length, the data is then sent to another pipelinefor data analysis.

The client software 311 may communicate with remote servers, such as aremote server/data analytics unit 401. The client software 311 may sendthe recorded three dimensional position data along with otherinformation 321, such as login credentials like a user identificationnumber and password, to the remote server/data analytics unit 401. Inthis embodiment, the remote server/data analysis unit 401 may calculatevarious tremor characteristics and features useful in quantifyingtremor, where the analysis may follow pseudocode similar to FIG. 14. Inthis embodiment, the analysis includes checking the recorded data todetermine whether the hand tracking system 101 has a consistent framerate and the data samples are evenly spaced in time. If the handtracking system 101 does not have a consistent sampling rate, apolynomial spline may be fit to the data set and then resampled at aconstant sampling rate to estimate the position signals in the threedimensions. Once the data has a constant sampling rate and the datapoints are evenly spaced in time, the coordinate system used to recordthe positions is translated so that equal weight is given to the threeaxes, when displacement from origin is calculated. For example, thefirst data point of three dimensional position in (x, y, z) recorded maybe (200 mm, 100 mm, 600 mm) in relation to the origin of the handtracking system 101, but this point would be translated to a positionsuch as (200 mm, 200 mm, 200 mm) and the same equations used for thistranslation would be appropriately applied to the rest of the data. Oncetranslated, the position data may be used to calculate the Euclideandistance, based on the three-dimensional position, from this translatedorigin for each frame. The difference between the Euclidean distancesmay then be calculated, between frames to determine the change indistance from the translated origin, to determine the change in positionover time. The translated position data may also be used to calculatethe displacements between frames for each individual axis. Thedifferences in the Euclidean distance and displacements for each axismay then be checked for data integrity, by checking for statisticalanomalies. For example, one may accomplish this by checking for valuesthat exceed the mean of the values with the addition of a certain numberof standard deviations or removing values that are below the mean withthe subtraction of a certain number of standard deviations. This datamay then be filtered using a band-pass filter to isolate the frequenciesof interest and then converted to the frequency domain using aFast-Fourier transform or other method, or may be performed in thereverse order. Once in the frequency domain the signal may be smoothedusing either adaptive or non-adaptive smoothing techniques. The smoothedsignal may then be used to calculate various tremor characteristics suchas, but not limited to peak amplitude, peak frequency, frequencydispersion, and proportional power within various frequency bands.

In addition to the hand tracking system 101, the embodiment may, but notnecessarily, include a smart watch/smart phone system (e.g., clientdevice 201). The smart watch/smart phone system 201 may include a smartphone, a smart watch, or both. A smart watch may refer to a wearabletechnology worn on the wrist, hand, leg, or finger (or any other bodypart) that contains one or more computing units with a CPU and memoryand one or more communication units such as but not limited to Wi-Fi,Bluetooth, or cellular modem units (3G, 4G, LTE) capable of relayinginformation wirelessly. This smart watch/smart phone system 201 mayinclude sensors for measuring motion such as but not limited toaccelerometers and gyroscopes. Further, the smart watch/smart phonesystem 201 may contain mobile software or a mobile application 204 toprovide a user interface that allows the user to enter log incredentials, view current and past information graphically ornumerically, and provides access to data from onboard sensors, shouldthey exist. In an embodiment, information 211 is sent from the smartwatch/smart phone system 201, which may include login credentials aswell as motion data from onboard sensors, to a remote server/dataanalysis unit 401 for data analysis. In yet another embodiment, datacollected from the smart watch and/or smart watch/phone system 201 maybe combined and analyzed together in the data analysis unit 401.

In an embodiment, the results of the data analysis as well as any otherinformation 411, such as login credentials, may then be electronicallysent from the remote server/data analytics unit 401 to the database 501.The information 411 sent may contain log in credentials that can be usedto determine permissions within the database 501 and to determine wherein the database 501 the information 411 should be saved.

An embodiment also includes communication between the client software311 and the database 501. The client software 311 or mobile software 204may query the database 501 using the proper credentials to gain accessto results and information stored associated with the user'scredentials. This information 511 may then be electronically sent fromthe database 501 to the client software 311 or mobile software 204.

In yet another embodiment, where the database 501 includes an EHRdatabase, the database 501 may contain information relevant to earlytherapeutic intervention. The database 501 may further contain orconnect to systems and data sources used in clinical research such aspharmaceutical development. Tremor data collected from the hand trackingsystem 101 may be stored in the database 501 and/or said clinicalresearch system or data source, alongside clinical profiling data suchas drug, dose, and patient characteristics. Tremor data may then beanalyzed to determine a signal of therapeutic effect such as therapeutictarget engagement in-vivo. Tremor data may further be combined withother markers such as genetic information, in-vivo imaging, lab tests,to determine such signals or profiles of efficacy. In yet anotherembodiment, such combined data is tremor data collected from the handtracking system 101 and then combined with data from a smart watchand/or smart watch/phone system 201. Said signals or profiles may thenbe used to identify promising clinical drug candidates and stored in yetanother database, analyzed by drug or between drugs to discoversimilarities. Machine learning, artificial intelligence (AI) orstatistical approaches may further be utilized to combine said markersor discover said similarities. In yet another embodiment, said tremordata is used alone or in combination with other markers to screen forand diagnosis of early disease such as prodromal Parkinson's Disease(PD), or for enrichment in a clinical drug or therapeutic device (suchas DBS) trial by selecting those patients that are most likely torespond, or identifying which patients are most likely to developdementia, in addition to developing motor symptoms. For example, anolfactory (sniff) test and/or genetic data could be combined with tremorassessment as disclosed to direct patients to more invasive andconfirmatory tests such as DAT imaging using SPECT, or PET imaging.Moreover, the tremor data as disclosed may further be utilized in anin-office setting as well as for home and portable monitoring oftherapeutic interventions. In yet another embodiment, tremor data may becompared to other markers such as in-vivo imaging data and/or geneticdata to determine certain tremor characteristics that can be used inpatient therapeutic monitoring.

Modifications, additions, or omissions may be made to the environment100 without departing from the scope of the present disclosure.

FIGS. 2-3 illustrate sequence diagrams of example methods related to ameasuring and analyzing tremor in various systems. The methods may beperformed by processing logic that may include hardware (circuitry,dedicated logic, etc.), software (such as is run on a general purposecomputer system or a dedicated machine), or a combination of both, whichprocessing logic may be included in the operating environment 100 ofFIG. 1, or another computer system or device. However, another system,or combination of systems, may be used to perform the methods. Forsimplicity of explanation, methods described herein are depicted anddescribed as a series of acts. However, acts in accordance with thisdisclosure may occur in various orders and/or concurrently, and withother acts not presented and described herein. Further, not allillustrated acts may be used to implement the methods in accordance withthe disclosed subject matter. In addition, those skilled in the art willunderstand and appreciate that the methods may alternatively berepresented as a series of interrelated states via a state diagram orevents. Additionally, the methods disclosed in this specification arecapable of being stored on an article of manufacture, such as anon-transitory computer-readable medium, to facilitate transporting andtransferring such methods to computing devices. The term article ofmanufacture, as used herein, is intended to encompass a computer programaccessible from any computer-readable device or storage media. Althoughillustrated as discrete blocks, various blocks may be divided intoadditional blocks, combined into fewer blocks, or eliminated, dependingon the desired implementation.

FIG. 2 illustrates a sequence diagram of an example method 200 tomeasure and analyze tremor in a system that includes a peripheralmeasurement device 101 connected to a client device 301. The method 200may be performed, at least in part, by a peripheral device (such as theclient device 101), a client (such as the client device 301) and aserver (such as the server 401).

At 250, the peripheral device 101 may be initialized with the clientdevice 301. In at least one embodiment, a subject uses a limb trackingdevice 101 and initializes the limb tracking device 101 with softwareoperating on a user's computer 301. In at least one embodiment, theclient 301 receives information about the user, such as via a userinterface. In at least one embodiment, user information has previouslybeen loaded to the peripheral 101 and the client 301 may receive thisuser information during the initialization.

At 252, the client 301 may initialize a wired or wireless connection toa server associated with a limb tracking system. The client 301 may beconfigured to send at least some of the user information to the server401. In at least one embodiment, the user information sent to the servermay not personally identify the user. The server 401 may identify a setof predetermined tasks, the performance of which may help to measure andanalyze tremor in the user. The server 401 may send the set ofpredetermined tasks to the client 301.

At 254 the client 301 may guide the user through the set ofpredetermined tasks. In at least one embodiment, the client 301 mayprompt the user through a series of tasks, such as via a visual userinterface. The tasks may include limb (e.g., hand, foot) movements ormotions to be performed by the user in various directions and forvarious periods of time. The user may perform the series of tasks whilewearing the or in range of (for non-wearable peripherals such as 3Dcamera) the peripheral 101 such that the peripheral 101 records themotions made by the user.

At 256 the peripheral 101 captures data based on the user's performanceof the set of predetermined tasks. At 258 the peripheral 101 sends thecaptured data to the client 301.

At 260 the client 301 may analyze the data received from the peripheral101. The client 301 may perform various analyses on the data. The client301 may perform the various analyses on the data in real-time. In atleast one embodiment, the client 301 may perform the various analyses onthe data to ensure the validity of the data and to ensure that the useris correctly performing the tasks as directed.

At 262 the client 301 sends the data to the server 401. In at least oneembodiment, the client 301 sends the data to the server after successfulvalidation and/or a determination that the user has correctly performingall of the tasks as directed.

At 264 the server 401 may analyze the data, as described herein. In atleast one embodiment, data analysis is performed by the server 401 toquantify a severity of tremor.

At 266 the server 401 may store the data and the results of theanalysis. In at least one embodiment, the results of data analysis aretransmitted from the server to the database, such as an EHR database forstorage.

At 268 the results from the data analysis are sent to the client 301.The results may be sent directly from the server 401 or from thedatabase such as an EHR database.

At 270 the client 301 may display the results of the data analysis tothe user, such as via a graphical user interface. The results may bedisplayed numerically, graphically, pictorially, etc.

FIG. 3 illustrates a sequence diagram of an example method 300 tomeasure and analyze tremor in a system that includes a client device 201with an integrated peripheral measurement device. The method 300 may beperformed, at least in part, by a client device (such as the clientdevice 201) and a server (such as the server 401).

At 350, an application at the client device 201 may be initialized. Inat least one embodiment, the client device 201 includes a smart watchand/or a smart phone system. The client device 201 may at leastpartially initialize the application running on the client device 201,such as by collecting and/or requesting user information. In at leastone embodiment, the client 201 receives information about the user, suchas via a user interface. In at least one embodiment, user informationhas previously been loaded to the client 201 may receive this userinformation during the initialization.

At 352, the application initializes a connection to hardware of theclient device 201, such as a tri-axial accelerometer or 3D camerathrough an available SDK.

At 354 the application guides the user through a set of predeterminedtasks. The performance of the set of predetermined tasks may help theclient device 201 to measure and analyze tremor in the user. The server401 may send the set of predetermined tasks to the client 201 or the setof predetermined tasks may be preloaded onto the client device 201. Inat least one embodiment, the client 201 may prompt the user through aseries of tasks, such as via a visual user interface. The tasks mayinclude limb (e.g., hand, foot) movements or motions to be performed bythe user in various directions and for various periods of time. The usermay perform the series of tasks while in range of a 3D camera, or bywearing the client 201, while holding the client 201, while the client201 is in the user's pant pocket, etc., or any other mechanism to couplethe client 201 to the user such that the client 201 records the motionsmade by the user.

At 356 the client 201 captures data based on the user's performance ofthe set of predetermined tasks. At 362 the client 201 sends the captureddata to the server 401.

At 364 the server 401 may analyze the data, as described herein. In atleast one embodiment, data analysis is performed by the server 401 toquantify a severity of tremor. The server 401 may perform the variousanalyses on the data substantially in real-time. In at least oneembodiment, the server 401 may perform the various analyses on the datato ensure the validity of the data and to ensure that the user iscorrectly performing the tasks as directed.

At 366 the server 401 may store the data and the results of theanalysis. In at least one embodiment, the results of data analysis aretransmitted from the server 401 to the database such as an EHR databasefor storage.

At 368 the results from the data analysis are sent to the client 201.The results may be sent directly from the server 401 or from the EHRdatabase.

At 370 the client 201 may display the results of the data analysis tothe user, such as via a graphical user interface. The results may bedisplayed numerically, graphically, pictorially, etc.

FIG. 4 illustrates a simulator system 400. To qualify sensors for use indrug development and subsequent clinical application, according to oneembodiment, a simulator 121 may be employed. The accuracy of the handtracking system 101 may be verified as depicted in the simplified blockdiagram of FIG. 4.

A simulator 121, with an embodiment shown in FIG. 5, may be used tosimulate human tremor. In this embodiment, a hand phantom 122 issecurely mounted on a platform in front of a hand tracking system (e.g.,client 101). The hand phantom 122 and platform may be secured to asliding track 124, to limit friction and limit movement to one axis. Thehand phantom 122 and platform may further be attached to a linearactuator 123, to move the hand phantom 122 in a prespecified motion. Thelinear actuator 123 may be controlled via a control board 125 or othermethod that allows for reproducible motion. One example of such acontrol board is marketed as “Linear Actuator Control Board” by FirgelliTechnologies, Inc. (Victoria BC, Canada). In the embodiment the slidingtrack 124, the linear actuator 123, and the hand tracking system 101 maybe secured to a mounting board 126 for example, to provide betterreproducibility and portability. The simulator creates a reproduciblemovement 131 that may be analyzed using an alternate tracking system141. This alternate tracking system 141 may include calculations fromthe linear actuator 123, control board 125, a high-resolution camera,laser measurement system, or other method independent of the handtracking system 101. The resulting positions 151 obtained or calculatedfrom the alternate tracking system 141 and resulting positions 111 fromthe hand tracking system 101 may then be compared with one another 161to determine the accuracy and reproducibility of the hand trackingsystem 101 as a method of qualifying said sensors and/or hand trackingsystem. Similarly, the accuracy and reproducibility of a wearable sensor(e.g., client device 201 in FIG. 1) may be verified by mounting thesensor on the hand phantom 122. In yet another embodiment, the alternatetracking system is an already qualified reference hand tracking systemthat is then used to qualify additional hand tracking systems that willbe distributed for use in clinical drug development or clinicalpractice.

By way of example, FIGS. 6-12 illustrate sample results for saidcomparison 161. One method of comparison 161 is a measurement of thereal positions 131 of the simulator while the simulator is turned offand the hand phantom is at rest, where it is known that true values ofthe positions 131 are constant, resulting in no displacement. Therefore,the null hypothesis would be that the data follows a normal distributionwith a mean displacement of zero and zero variance.

FIG. 6 illustrates sample data that may include 2297 samples that wereobtained using a simulator with an embodiment similar to that of FIG. 5and a “Leap Motion Controller” for the hand tracking system 101, wherethe phantom hand 122 remained stationary during the recording. Thedistribution of the displacement of the magnitudes of the three axes ofpositions passed the conditions of a normal distribution.

FIG. 7 illustrates a two-sample t-test of the dataset shown in FIG. 6against a dataset with a zero mean and zero variance at 0.01significance level, where the critical T-value is 2.6. As demonstratedin FIG. 7, the sample data supports the null hypothesis that the datashown in FIG. 6 is a part of a dataset that includes a zero mean andzero variance.

Another possible method of comparison 161 includes a tremorcharacteristic that measures and calculates the dominant frequency of agiven movement, after it has been filtered.

FIG. 8 illustrates sample results again using a “Leap Motion Controller”as the hand tracking system 101 and a simulator with an embodimentsimilar to that of FIG. 5. These sample results may include 20 differentrecordings lasting 20 seconds each at three different frequencies, wherethe dominant frequency was pre-determined using a control board 125 andverified using high-resolution video as the alternate tracking system141. The calculated frequencies from the “Leap Motion Controller” werecompared to the known predetermined frequencies and plotted to determinerepeatability and accuracy. Similarly, FIG. 10 illustrates differentsample results using the same method mentioned, but instead may include10 different recordings lasting 10 seconds each for three differentfrequencies. Using these different lengths of recordings may assist indetermining whether utilizing less data, from a shorter test, stillprovides accurate results for determining the dominant frequency. FIG.12 illustrates a plot of the aggregation of different predetermineddominant frequencies and how they compare to the calculated dominantfrequency, using the before mentioned method. A linear regression may befit to the data, where the closer the R-squared value of the linearregression is to a value of one the more accurate the calculateddominant frequencies are to the known predetermined dominantfrequencies. As demonstrated in FIG. 12, using the “Leap MotionController”, the calculated dominant frequencies are shown to have anear perfect linear regression with an R-squared value of approximately0.99694 suggesting that the calculated dominant frequencies areaccurate.

Yet another method of comparison 161 may include a tremor characteristicthat involves measuring and calculating an estimation of the amplitudeof displacement of a given movement at the dominant frequency afterfiltering. FIG. 9 illustrates sample results again using a “Leap MotionController” as the hand tracking system 101 and a simulator with anembodiment similar to that of FIG. 5. These sample results may include20 different recordings lasting 20 seconds each at three differentamplitudes, where the amplitude at a dominant frequency waspre-determined using a control board 125 and verified usinghigh-resolution video as the alternate tracking system 141. Thecalculated amplitudes at the dominant frequency from the “Leap MotionController” were compared to the known predetermined amplitudes andplotted to determine repeatability and accuracy of measuring theamplitude of displacement at the dominant frequency. Similarly, FIG. 11illustrates different sample results using the same method mentioned,but instead may include ten different recordings lasting ten secondseach for three different known amplitudes. Using these different lengthsof recordings may assist in determining whether utilizing less data,from a shorter test, still provides accurate results for determining thedominant amplitude.

The before mentioned methods of comparison 161 are only some particularexamples of methods used to verify and validate the accuracy andreproducibility of the hand tracking system 101 against an alternatetracking system 141.

FIG. 15 illustrates a diagrammatic representation of a machine in theexample form of a computing device 1500 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed herein, may be executed. The computing device1500 may include a mobile phone, a smart phone, a netbook computer, arackmount server, a router computer, a server computer, a personalcomputer, a mainframe computer, a laptop computer, a tablet computer, adesktop computer etc., within which a set of instructions, for causingthe machine to perform any one or more of the methods discussed herein,may be executed. In alternative embodiments, the machine may beconnected (e.g., networked) to other machines in a LAN, an intranet, anextranet, or the Internet. The machine may operate in the capacity of aserver machine in client-server network environment. The machine may bea personal computer (PC), a set-top box (STB), a server, a networkrouter, switch or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that machine. Further, while only a single machine is illustrated,the term “machine” may also include any collection of machines thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methods discussed herein.

The example computing device 1500 includes a processing device (e.g., aprocessor) 1502, a main memory 1504 (e.g., read-only memory (ROM), flashmemory, dynamic random access memory (DRAM) such as synchronous DRAM(SDRAM)), a static memory 1506 (e.g., flash memory, static random accessmemory (SRAM)) and a data storage device 1516, which communicate witheach other via a bus 1508.

Processing device 1502 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 1502 may be a complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, or a processor implementing other instruction sets orprocessors implementing a combination of instruction sets. Theprocessing device 1502 may also be one or more special-purposeprocessing devices such as an application specific integrated circuit(ASIC), a field programmable gate array (FPGA), a digital signalprocessor (DSP), network processor, or the like. The processing device1502 is configured to execute instructions 1526 for performing theoperations and steps discussed herein.

The computing device 1500 may further include a network interface device1522 which may communicate with a network 1518. The computing device1500 also may include a display device 1510 (e.g., a liquid crystaldisplay (LCD) or a cathode ray tube (CRT)), an alphanumeric input device1512 (e.g., a keyboard), a cursor control device 1514 (e.g., a mouse)and a signal generation device 1520 (e.g., a speaker). In oneimplementation, the display device 1510, the alphanumeric input device1512, and the cursor control device 1514 may be combined into a singlecomponent or device (e.g., an LCD touch screen).

The data storage device 1516 may include a computer-readable storagemedium 1524 on which is stored one or more sets of instructions 1526embodying any one or more of the methodologies or functions describedherein. The instructions 1526 may also reside, completely or at leastpartially, within the main memory 1504 and/or within the processingdevice 1502 during execution thereof by the computing device 1500, themain memory 1504 and the processing device 1502 also constitutingcomputer-readable media. The instructions may further be transmitted orreceived over a network 1518 via the network interface device 1522.

While the computer-readable storage medium 1524 is shown in an exampleembodiment to be a single medium, the term “computer-readable storagemedium” may include a single medium or multiple media (e.g., acentralized or distributed database and/or associated caches andservers) that store the one or more sets of instructions. The term“computer-readable storage medium” may also include any medium that iscapable of storing, encoding or carrying a set of instructions forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present disclosure. The term“computer-readable storage medium” may accordingly be taken to include,but not be limited to, solid-state memories, optical media and magneticmedia.

In one aspect, a system includes a memory. The system further includes acommunications interface. The system further includes at least onesensor to measure a signal associated with position or motion of anextremity of a subject. The system further includes a processoroperatively coupled to the memory, the communications interface, and thesensor. The processor is configured to perform operations includesreceive the signal from the sensor. The processor is configured tocommunicate, via the communications interface, the signal, logincredentials, and position or motion data to a remote server coupled to aremote server. The remote server is configured to receive the signal andperform an analysis that quantifies a severity of tremor based on theposition or motion signals of the subject. The processor is configuredto receive the analysis that quantified the severity of tremor. Theprocessor is configured to cause the quantified severity of tremor to bedisplayed via a display device.

Implementations can include any, all, or none of the following features.One of the sensors can be not in physical contact with the subject torecord the position or motion of the subject. Said measurement of thesubject's tremor can be applied to early therapeutic intervention inParkinson's Disease. The processor can be further configured to at leastpartially identify a clinical drug candidate in pharmaceuticaldevelopment by confirming therapeutic target engagement in-vivo, b)screen for and diagnose early disease such as prodromal Parkinson'sDisease, and c) enable in-office as well as home and portable monitoringof therapeutic interventions. The remote server can be coupled to anelectronic health records database. The electronic health recordsdatabase can be configured to store and maintain the subject's resultinganalysis and signal from the remote server for an individual userassociated with login credentials for identity verification. The remoteserver can be configured to send the resulting analysis to theelectronic health records database. The electronic health recordsdatabase can include a records server that can be configured to generatea database, in response to the receipt of subject results and analysisfrom the remote server. The records server can include a processor toperform operations that can include maintain in memory the signalsreceived from the remote server that can include the analysis of andsignals used to quantify tremor severity. The processor can furtherperform operations that include generate a database that can includesubject data that can be associated and stored, according to the logincredentials. The processor can further perform operations that includeoutput, as a result of a query, the analysis and signals from thedatabase in electronic form to a remote platform for viewing providedthat correct login credentials can be provided. The sensor can bequalified for accuracy using a simulator that can include a phantomcapable to produce a reproducible movement and comparing the results ofthe external sensor with an alternate method of measuring position andmotion. The qualification can be for early therapeutic intervention inParkinson's Disease.

In one aspect, a system includes a memory. The system further includes acommunications interface. The system further includes a processoroperatively coupled to the memory, the communications interface, and aperipheral sensor. The peripheral sensor is configured to measure asignal associated with position or motion of an extremity of a subject.The processor is configured to perform operations includes receive asignal from the peripheral sensor. The processor is configured tocommunicate, via the communications interface, the signal, logincredentials, and position or motion data to a remote server coupled to aremote server. The remote server is configured to receive the signal andperform an analysis that quantifies a severity of tremor based on theposition or motion signals of the subject. The processor is configuredto receive the analysis that quantified the severity of tremor. Theprocessor is configured to cause the quantified severity of tremor to bedisplayed via a display device.

Implementations can include any, all, or none of the following features.The sensor can be not in physical contact with the subject to record theposition or motion of the subject. Said measurement of a subject'stremor can be applied to early therapeutic intervention in Parkinson'sDisease. The processor can be further configured to at least partiallyidentify a clinical drug candidate in pharmaceutical development byconfirming therapeutic target engagement in-vivo. The processor can befurther configured to at least partially screen for and diagnose earlydisease such as prodromal Parkinson's Disease. The processor can befurther configured to enable in-office and home and portable monitoringof therapeutic interventions. The remote server can be coupled to anelectronic health records database. The electronic health recordsdatabase can be configured to store and maintain the subject's resultinganalysis and signal from the remote server for an individual userassociated with the login credentials for identity verification. Theremote server can be configured to send the resulting analysis to theelectronic health records database. The electronic health recordsdatabase can include a records server that can be configured to generatea database, in response to the receipt of subject results and analysisfrom the remote server. The records server can include a processor toperform operations can include maintain in memory the signals receivedfrom the remote server that can include the analysis of and signals usedto quantify tremor severity. The processor is configured to generate adatabase that can include individual data that can be associated andstored, according to the login credentials. The processor is configuredto output, as a result of a query, the analysis and signals from thedatabase in electronic form to a remote platform for viewing providedthat correct login credentials can be provided. The sensor can bequalified for accuracy using a simulator that can include a phantomcapable to produce a reproducible movement and comparing the results ofthe external sensor with an alternate method of measuring position andmotion. The qualification can be for early therapeutic intervention inParkinson's Disease.

In one aspect, a method includes receiving, from at least one sensor, asignal indicative of a change in position or motion of an extremity of asubject. The method further includes communicating, via a communicationsinterface, the signal, login credentials, and position or motion data toa remote server coupled to a remote electronic health record database.The remote server is configured to receive the signal and perform ananalysis that quantifies a severity of tremor based on the position ormotion signals of the subject. The method further includes receiving theanalysis that quantified the severity of tremor. The method furtherincludes causing the quantified severity of tremor to be displayed via adisplay device.

Implementations can include any, all, or none of the following features.One of the sensors can be not in physical contact with the subject torecord the position or motion of the subject.

Terms used herein and especially in the appended claims (e.g., bodies ofthe appended claims) are generally intended as “open” terms (e.g., theterm “including” may be interpreted as “including, but not limited to,”the term “having” may be interpreted as “having at least,” the term“includes” may be interpreted as “includes, but is not limited to,”etc.).

Additionally, if a specific number of an introduced claim recitation isintended, such an intent will be explicitly recited in the claim, and inthe absence of such recitation no such intent is present. For example,as an aid to understanding, the following appended claims may containusage of the introductory phrases “at least one” and “one or more” tointroduce claim recitations. However, the use of such phrases may not beconstrued to imply that the introduction of a claim recitation by theindefinite articles “a” or “an” limits any particular claim containingsuch introduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” may be interpreted to mean “at least one” or“one or more”); the same holds true for the use of definite articlesused to introduce claim recitations.

In addition, even if a specific number of an introduced claim recitationis explicitly recited, those skilled in the art will recognize that suchrecitation may be interpreted to mean at least the recited number (e.g.,the bare recitation of “two recitations,” without other modifiers, meansat least two recitations, or two or more recitations). Further, in thoseinstances where a convention analogous to “at least one of A, B, and C,etc.” or “one or more of A, B, and C, etc.” is used, in general such aconstruction is intended to include A alone, B alone, C alone, A and Btogether, A and C together, B and C together, or A, B, and C together,etc. For example, the use of the term “and/or” is intended to beconstrued in this manner.

Further, any disjunctive word or phrase presenting two or morealternative terms, whether in the description, claims, or drawings, maybe understood to contemplate the possibilities of including one of theterms, either of the terms, or both terms. For example, the phrase “A orB” may be understood to include the possibilities of “A” or “B” or “Aand B.”

Embodiments described herein may be implemented using computer-readablemedia for carrying or having computer-executable instructions or datastructures stored thereon. Such computer-readable media may be anyavailable media that may be accessed by a general purpose or specialpurpose computer. By way of example, and not limitation, suchcomputer-readable media may include non-transitory computer-readablestorage media including Random Access Memory (RAM), Read-Only Memory(ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM),Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage,magnetic disk storage or other magnetic storage devices, flash memorydevices (e.g., solid state memory devices), or any other storage mediumwhich may be used to carry or store desired program code in the form ofcomputer-executable instructions or data structures and which may beaccessed by a general purpose or special purpose computer. Combinationsof the above may also be included within the scope of computer-readablemedia.

Computer-executable instructions may include, for example, instructionsand data which cause a general purpose computer, special purposecomputer, or special purpose processing device (e.g., one or moreprocessors) to perform a certain function or group of functions.Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

As used herein, the terms “module” or “component” may refer to specifichardware implementations configured to perform the operations of themodule or component and/or software objects or software routines thatmay be stored on and/or executed by general purpose hardware (e.g.,computer-readable media, processing devices, etc.) of the computingsystem. In some embodiments, the different components, modules, engines,and services described herein may be implemented as objects or processesthat execute on the computing system (e.g., as separate threads). Whilesome of the system and methods described herein are generally describedas being implemented in software (stored on and/or executed by generalpurpose hardware), specific hardware implementations or a combination ofsoftware and specific hardware implementations are also possible andcontemplated. In this description, a “computing entity” may be anycomputing system as previously defined herein, or any module orcombination of modulates running on a computing system.

All examples and conditional language recited herein are intended forpedagogical objects to aid the reader in understanding the invention andthe concepts contributed by the inventor to furthering the art, and areto be construed as being without limitation to such specifically recitedexamples and conditions. Although embodiments of the present disclosurehave been described in detail, it may be understood that the variouschanges, substitutions, and alterations may be made hereto withoutdeparting from the spirit and scope of the present disclosure.

What is claimed is:
 1. A system, comprising: a memory; a communicationsinterface; at least one sensor to measure a signal associated withposition or motion of an extremity of a subject, wherein the at leastone sensor includes a portable optical sensor and wherein the portableoptical sensor is qualified for accuracy by: using a tremor simulatordevice that includes a hand phantom capable to produce a simulatedmovement of a human hand; measuring movement of the hand phantom usingthe at least one sensor and an alternate method of measuring positionand motion; comparing the results of the at least one sensor with thealternate method of measuring position and motion; and determining toqualify the at least one sensor based on the comparison of the resultsand based on at least one statistical analysis metric indicative ofreproducible accuracy, wherein the at least one sensor is qualifiedbased on the at least one statistical analysis metric meeting apredetermined threshold; and a processor operatively coupled to thememory, the communications interface, and the sensor, wherein theprocessor is configured to perform operations comprising: receive thesignal from the sensor; communicate, via the communications interface,the signal, login credentials, and position or motion data to a remoteserver, wherein the remote server is configured to receive the signaland perform an analysis that quantifies a severity of tremor based onthe position or motion signals of the subject; receive the analysis thatquantified the severity of tremor.
 2. The system of claim 1, furthercomprising causing the quantified severity of tremor to be displayed viaa display device.
 3. The system of claim 2, wherein the quantifiedseverity of tremor is compared to at least one reference value, andbased on the comparison, further color coded when displayed via thedisplay device.
 4. The system of claim 1, wherein the optical sensorincludes a depth camera or 3D camera.
 5. The system of claim 1, whereinthe system is further applied to early therapeutic intervention.
 6. Thesystem of claim 5, wherein the early therapeutic intervention includes anon-drug early intervention approach.
 7. The system of claim 6, whereinthe non-drug early intervention approach includes brain stimulation. 8.The system of claim 7, wherein the brain stimulation includes one ormore of: deep brain stimulation (DBS), Transcranial magnetic stimulation(TMS), transcranial direct current stimulation (tDCS), transcutaneouselectrical nerve stimulation (TENS).
 9. The system of claim 6, whereinthe non-drug early intervention approach is focused ultrasound.
 10. Amethod, comprising: receiving, from at least one sensor, a signalindicative of a change in position or motion of an extremity of asubject, wherein one sensor of the at least one sensor includes anoptical sensor or a wearable sensor, wherein the at least one sensor isqualified for accuracy by: using an artificial hand tremor simulatorthat includes a human hand phantom capable to produce a reproduciblemovement; comparing the results of the at least one sensor with analternate method of measuring position and motion; and determining toqualify the at least one sensor based on the comparison of the results;and communicating, via a communications interface, the signal to aprocessor coupled to a remote electronic health record database, whereinthe processor is configured to receive the signal and perform ananalysis that quantifies a severity of tremor based on the position ormotion signals of the subject.
 11. The method of claim 10, furthercomprising causing the quantified severity of tremor to be displayed viaa display device
 12. The method of claim 11, wherein the quantifiedseverity of tremor is compared to at least one reference value, andbased on the comparison, further color coded when displayed via thedisplay device.
 13. The method of claim 10, wherein the optical sensorincludes a depth camera or 3D camera.
 14. The method of claim 10,wherein the method is applied to early therapeutic intervention.
 15. Themethod of claim 14, wherein the early therapeutic intervention includesa non-drug early intervention approach
 16. The method of claim 15,wherein the non-drug early intervention approach includes brainstimulation.
 17. The method of claim 16, wherein the brain stimulationincludes one or more of: deep brain stimulation (DBS), Transcranialmagnetic stimulation (TMS), transcranial direct current stimulation(tDCS), transcutaneous electrical nerve stimulation (TENS).
 18. Themethod of claim 15, wherein the non-drug early intervention approach isfocused ultrasound.
 19. The method of claim 10, wherein the quantifiedtremor severity is further combined with an olfactory test and/orgenetic data to direct patients to positron emission tomography (PET)imaging.
 20. The method of claim 10, further comprising: at leastpartially identifying a clinical drug candidate in pharmaceuticaldevelopment by confirming therapeutic target engagement in-vivo; atleast partially screening for and diagnosing early disease such asprodromal Parkinson's Disease; or enabling in-office and home andportable monitoring of therapeutic interventions.